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<li class="chapter" data-level="1" data-path="index.html"><a href="index.html"><i class="fa fa-check"></i><b>1</b> 机器学习入门指南（极简版）</a><ul>
<li class="chapter" data-level="1.1" data-path="index.html"><a href="index.html#python"><i class="fa fa-check"></i><b>1.1</b> Python</a><ul>
<li class="chapter" data-level="1.1.1" data-path="index.html"><a href="index.html#python书"><i class="fa fa-check"></i><b>1.1.1</b> Python——书</a></li>
<li class="chapter" data-level="1.1.2" data-path="index.html"><a href="index.html#python教程"><i class="fa fa-check"></i><b>1.1.2</b> Python——教程</a></li>
<li class="chapter" data-level="1.1.3" data-path="index.html"><a href="index.html#python视频"><i class="fa fa-check"></i><b>1.1.3</b> Python——视频</a></li>
</ul></li>
<li class="chapter" data-level="1.2" data-path="index.html"><a href="index.html#机器学习"><i class="fa fa-check"></i><b>1.2</b> 机器学习</a><ul>
<li class="chapter" data-level="1.2.1" data-path="index.html"><a href="index.html#机器学习书"><i class="fa fa-check"></i><b>1.2.1</b> 机器学习——书</a></li>
<li class="chapter" data-level="1.2.2" data-path="index.html"><a href="index.html#机器学习教程"><i class="fa fa-check"></i><b>1.2.2</b> 机器学习——教程</a></li>
<li class="chapter" data-level="1.2.3" data-path="index.html"><a href="index.html#机器学习视频"><i class="fa fa-check"></i><b>1.2.3</b> 机器学习——视频</a></li>
<li class="chapter" data-level="1.2.4" data-path="index.html"><a href="index.html#机器学习数学基础"><i class="fa fa-check"></i><b>1.2.4</b> 机器学习——数学基础</a></li>
</ul></li>
<li class="chapter" data-level="1.3" data-path="index.html"><a href="index.html#一些经验和建议"><i class="fa fa-check"></i><b>1.3</b> 一些经验和建议</a></li>
</ul></li>
<li class="chapter" data-level="2" data-path="python基础.html"><a href="python基础.html"><i class="fa fa-check"></i><b>2</b> Python基础</a><ul>
<li class="chapter" data-level="2.1" data-path="python基础.html"><a href="python基础.html#python-1"><i class="fa fa-check"></i><b>2.1</b> Python</a><ul>
<li class="chapter" data-level="2.1.1" data-path="python基础.html"><a href="python基础.html#python学习教程"><i class="fa fa-check"></i><b>2.1.1</b> Python学习教程</a></li>
<li class="chapter" data-level="2.1.2" data-path="python基础.html"><a href="python基础.html#python学习方法"><i class="fa fa-check"></i><b>2.1.2</b> Python学习方法</a></li>
<li class="chapter" data-level="2.1.3" data-path="python基础.html"><a href="python基础.html#python基础系列"><i class="fa fa-check"></i><b>2.1.3</b> Python基础系列</a></li>
<li class="chapter" data-level="2.1.4" data-path="python基础.html"><a href="python基础.html#python库"><i class="fa fa-check"></i><b>2.1.4</b> Python库</a></li>
</ul></li>
<li class="chapter" data-level="2.2" data-path="python基础.html"><a href="python基础.html#numpy"><i class="fa fa-check"></i><b>2.2</b> Numpy</a></li>
<li class="chapter" data-level="2.3" data-path="python基础.html"><a href="python基础.html#pandas"><i class="fa fa-check"></i><b>2.3</b> Pandas</a></li>
<li class="chapter" data-level="2.4" data-path="python基础.html"><a href="python基础.html#matplotlib"><i class="fa fa-check"></i><b>2.4</b> Matplotlib</a></li>
<li class="chapter" data-level="2.5" data-path="python基础.html"><a href="python基础.html#python数据可视化"><i class="fa fa-check"></i><b>2.5</b> Python数据可视化</a></li>
<li class="chapter" data-level="2.6" data-path="python基础.html"><a href="python基础.html#环境和ide"><i class="fa fa-check"></i><b>2.6</b> 环境和IDE</a><ul>
<li class="chapter" data-level="2.6.1" data-path="python基础.html"><a href="python基础.html#如何选择ide"><i class="fa fa-check"></i><b>2.6.1</b> 如何选择IDE</a></li>
<li class="chapter" data-level="2.6.2" data-path="python基础.html"><a href="python基础.html#pycharm"><i class="fa fa-check"></i><b>2.6.2</b> PyCharm</a></li>
<li class="chapter" data-level="2.6.3" data-path="python基础.html"><a href="python基础.html#vscode"><i class="fa fa-check"></i><b>2.6.3</b> VSCode</a></li>
<li class="chapter" data-level="2.6.4" data-path="python基础.html"><a href="python基础.html#spyderjupyter"><i class="fa fa-check"></i><b>2.6.4</b> Spyder&amp;Jupyter</a></li>
</ul></li>
<li class="chapter" data-level="2.7" data-path="python基础.html"><a href="python基础.html#如何阅读-python-开源项目代码"><i class="fa fa-check"></i><b>2.7</b> 如何阅读 Python 开源项目代码?</a></li>
<li class="chapter" data-level="2.8" data-path="python基础.html"><a href="python基础.html#其他待分类"><i class="fa fa-check"></i><b>2.8</b> 其他（待分类）</a></li>
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<li class="chapter" data-level="3" data-path="数学基础.html"><a href="数学基础.html"><i class="fa fa-check"></i><b>3</b> 数学基础</a><ul>
<li class="chapter" data-level="3.1" data-path="数学基础.html"><a href="数学基础.html#数学学习误区"><i class="fa fa-check"></i><b>3.1</b> 数学学习误区</a></li>
<li class="chapter" data-level="3.2" data-path="数学基础.html"><a href="数学基础.html#机器学习与数学"><i class="fa fa-check"></i><b>3.2</b> 机器学习与数学</a></li>
<li class="chapter" data-level="3.3" data-path="数学基础.html"><a href="数学基础.html#统计学"><i class="fa fa-check"></i><b>3.3</b> 统计学</a></li>
<li class="chapter" data-level="3.4" data-path="数学基础.html"><a href="数学基础.html#概率论"><i class="fa fa-check"></i><b>3.4</b> 概率论</a></li>
<li class="chapter" data-level="3.5" data-path="数学基础.html"><a href="数学基础.html#微积分"><i class="fa fa-check"></i><b>3.5</b> 微积分</a></li>
<li class="chapter" data-level="3.6" data-path="数学基础.html"><a href="数学基础.html#线性代数"><i class="fa fa-check"></i><b>3.6</b> 线性代数</a></li>
<li class="chapter" data-level="3.7" data-path="数学基础.html"><a href="数学基础.html#优化"><i class="fa fa-check"></i><b>3.7</b> 优化</a></li>
</ul></li>
<li class="chapter" data-level="4" data-path="机器学习基础.html"><a href="机器学习基础.html"><i class="fa fa-check"></i><b>4</b> 机器学习基础</a><ul>
<li class="chapter" data-level="4.1" data-path="机器学习基础.html"><a href="机器学习基础.html#机器学习总览"><i class="fa fa-check"></i><b>4.1</b> 机器学习总览</a></li>
<li class="chapter" data-level="4.2" data-path="机器学习基础.html"><a href="机器学习基础.html#机器学习的局限"><i class="fa fa-check"></i><b>4.2</b> 机器学习的局限</a></li>
<li class="chapter" data-level="4.3" data-path="机器学习基础.html"><a href="机器学习基础.html#数据清理和格式化"><i class="fa fa-check"></i><b>4.3</b> 数据清理和格式化</a></li>
<li class="chapter" data-level="4.4" data-path="机器学习基础.html"><a href="机器学习基础.html#探索性数据分析"><i class="fa fa-check"></i><b>4.4</b> 探索性数据分析</a></li>
<li class="chapter" data-level="4.5" data-path="机器学习基础.html"><a href="机器学习基础.html#特征工程和特征选择"><i class="fa fa-check"></i><b>4.5</b> 特征工程和特征选择</a></li>
<li class="chapter" data-level="4.6" data-path="机器学习基础.html"><a href="机器学习基础.html#性能指标"><i class="fa fa-check"></i><b>4.6</b> 性能指标</a></li>
<li class="chapter" data-level="4.7" data-path="机器学习基础.html"><a href="机器学习基础.html#优化方法"><i class="fa fa-check"></i><b>4.7</b> 优化方法</a></li>
<li class="chapter" data-level="4.8" data-path="机器学习基础.html"><a href="机器学习基础.html#超参数调整"><i class="fa fa-check"></i><b>4.8</b> 超参数调整</a></li>
<li class="chapter" data-level="4.9" data-path="机器学习基础.html"><a href="机器学习基础.html#评估最佳模型"><i class="fa fa-check"></i><b>4.9</b> 评估最佳模型</a></li>
<li class="chapter" data-level="4.10" data-path="机器学习基础.html"><a href="机器学习基础.html#机器学习资源推荐"><i class="fa fa-check"></i><b>4.10</b> 机器学习资源推荐</a></li>
<li class="chapter" data-level="4.11" data-path="机器学习基础.html"><a href="机器学习基础.html#面试竞赛经验"><i class="fa fa-check"></i><b>4.11</b> 面试&amp;竞赛经验</a></li>
<li class="chapter" data-level="4.12" data-path="机器学习基础.html"><a href="机器学习基础.html#机器学习的书怎么读"><i class="fa fa-check"></i><b>4.12</b> 机器学习的书怎么读？</a><ul>
<li class="chapter" data-level="4.12.1" data-path="机器学习基础.html"><a href="机器学习基础.html#统计学习方法"><i class="fa fa-check"></i><b>4.12.1</b> 统计学习方法</a></li>
<li class="chapter" data-level="4.12.2" data-path="机器学习基础.html"><a href="机器学习基础.html#西瓜书"><i class="fa fa-check"></i><b>4.12.2</b> 西瓜书</a></li>
</ul></li>
<li class="chapter" data-level="4.13" data-path="机器学习基础.html"><a href="机器学习基础.html#机器学习工具"><i class="fa fa-check"></i><b>4.13</b> 机器学习工具</a></li>
<li class="chapter" data-level="4.14" data-path="机器学习基础.html"><a href="机器学习基础.html#其他"><i class="fa fa-check"></i><b>4.14</b> 其他</a></li>
</ul></li>
<li class="chapter" data-level="5" data-path="机器学习模型.html"><a href="机器学习模型.html"><i class="fa fa-check"></i><b>5</b> 机器学习模型</a><ul>
<li class="chapter" data-level="5.1" data-path="机器学习模型.html"><a href="机器学习模型.html#掌握机器学习算法的三重境界"><i class="fa fa-check"></i><b>5.1</b> 掌握机器学习算法的三重境界</a></li>
<li class="chapter" data-level="5.2" data-path="机器学习模型.html"><a href="机器学习模型.html#天搞定机器学习系统连载中"><i class="fa fa-check"></i><b>5.2</b> 100天搞定机器学习系统（连载中）</a></li>
<li class="chapter" data-level="5.3" data-path="机器学习模型.html"><a href="机器学习模型.html#回归"><i class="fa fa-check"></i><b>5.3</b> 回归</a></li>
<li class="chapter" data-level="5.4" data-path="机器学习模型.html"><a href="机器学习模型.html#逻辑回归"><i class="fa fa-check"></i><b>5.4</b> 逻辑回归</a></li>
<li class="chapter" data-level="5.5" data-path="机器学习模型.html"><a href="机器学习模型.html#决策树"><i class="fa fa-check"></i><b>5.5</b> 决策树</a></li>
<li class="chapter" data-level="5.6" data-path="机器学习模型.html"><a href="机器学习模型.html#主成分分析"><i class="fa fa-check"></i><b>5.6</b> 主成分分析</a></li>
<li class="chapter" data-level="5.7" data-path="机器学习模型.html"><a href="机器学习模型.html#随机森林"><i class="fa fa-check"></i><b>5.7</b> 随机森林</a></li>
<li class="chapter" data-level="5.8" data-path="机器学习模型.html"><a href="机器学习模型.html#xgboost"><i class="fa fa-check"></i><b>5.8</b> XGBoost</a></li>
<li class="chapter" data-level="5.9" data-path="机器学习模型.html"><a href="机器学习模型.html#聚类"><i class="fa fa-check"></i><b>5.9</b> 聚类</a></li>
<li class="chapter" data-level="5.10" data-path="机器学习模型.html"><a href="机器学习模型.html#贝叶斯"><i class="fa fa-check"></i><b>5.10</b> 贝叶斯</a></li>
<li class="chapter" data-level="5.11" data-path="机器学习模型.html"><a href="机器学习模型.html#svm"><i class="fa fa-check"></i><b>5.11</b> SVM</a></li>
<li class="chapter" data-level="5.12" data-path="机器学习模型.html"><a href="机器学习模型.html#降维"><i class="fa fa-check"></i><b>5.12</b> 降维</a></li>
<li class="chapter" data-level="5.13" data-path="机器学习模型.html"><a href="机器学习模型.html#其他-1"><i class="fa fa-check"></i><b>5.13</b> 其他</a></li>
<li class="chapter" data-level="5.14" data-path="机器学习模型.html"><a href="机器学习模型.html#学习方法"><i class="fa fa-check"></i><b>5.14</b> 学习方法</a></li>
</ul></li>
<li class="chapter" data-level="6" data-path="机器学习项目实战.html"><a href="机器学习项目实战.html"><i class="fa fa-check"></i><b>6</b> 机器学习项目实战</a><ul>
<li class="chapter" data-level="6.1" data-path="机器学习项目实战.html"><a href="机器学习项目实战.html#数据分析篇"><i class="fa fa-check"></i><b>6.1</b> 数据分析篇</a></li>
<li class="chapter" data-level="6.2" data-path="机器学习项目实战.html"><a href="机器学习项目实战.html#机器学习篇"><i class="fa fa-check"></i><b>6.2</b> 机器学习篇</a></li>
<li class="chapter" data-level="6.3" data-path="机器学习项目实战.html"><a href="机器学习项目实战.html#深度学习"><i class="fa fa-check"></i><b>6.3</b> 深度学习</a></li>
<li class="chapter" data-level="6.4" data-path="机器学习项目实战.html"><a href="机器学习项目实战.html#其他-2"><i class="fa fa-check"></i><b>6.4</b> 其他</a></li>
</ul></li>
<li class="chapter" data-level="7" data-path="深度学习基础.html"><a href="深度学习基础.html"><i class="fa fa-check"></i><b>7</b> 深度学习基础</a><ul>
<li class="chapter" data-level="7.1" data-path="深度学习基础.html"><a href="深度学习基础.html#入门教程"><i class="fa fa-check"></i><b>7.1</b> 入门教程</a></li>
<li class="chapter" data-level="7.2" data-path="深度学习基础.html"><a href="深度学习基础.html#神经网络"><i class="fa fa-check"></i><b>7.2</b> 神经网络</a></li>
<li class="chapter" data-level="7.3" data-path="深度学习基础.html"><a href="深度学习基础.html#深度学习-1"><i class="fa fa-check"></i><b>7.3</b> 深度学习</a></li>
<li class="chapter" data-level="7.4" data-path="深度学习基础.html"><a href="深度学习基础.html#资源推荐"><i class="fa fa-check"></i><b>7.4</b> 资源推荐</a></li>
<li class="chapter" data-level="7.5" data-path="深度学习基础.html"><a href="深度学习基础.html#其他-3"><i class="fa fa-check"></i><b>7.5</b> 其他</a></li>
</ul></li>
<li class="chapter" data-level="8" data-path="工具和框架篇.html"><a href="工具和框架篇.html"><i class="fa fa-check"></i><b>8</b> 工具和框架篇</a><ul>
<li class="chapter" data-level="8.1" data-path="工具和框架篇.html"><a href="工具和框架篇.html#常见框架"><i class="fa fa-check"></i><b>8.1</b> 常见框架</a></li>
<li class="chapter" data-level="8.2" data-path="工具和框架篇.html"><a href="工具和框架篇.html#sklearn"><i class="fa fa-check"></i><b>8.2</b> sklearn</a><ul>
<li class="chapter" data-level="8.2.1" data-path="工具和框架篇.html"><a href="工具和框架篇.html#如何正确地实用sklearn"><i class="fa fa-check"></i><b>8.2.1</b> 如何正确地实用sklearn</a></li>
<li class="chapter" data-level="8.2.2" data-path="工具和框架篇.html"><a href="工具和框架篇.html#sklearn入门及技巧篇"><i class="fa fa-check"></i><b>8.2.2</b> sklearn入门及技巧篇</a></li>
</ul></li>
<li class="chapter" data-level="8.3" data-path="工具和框架篇.html"><a href="工具和框架篇.html#tensorflow-vs-pytorch"><i class="fa fa-check"></i><b>8.3</b> TensorFlow VS PyTorch</a><ul>
<li class="chapter" data-level="8.3.1" data-path="工具和框架篇.html"><a href="工具和框架篇.html#安装问题"><i class="fa fa-check"></i><b>8.3.1</b> 安装问题</a></li>
</ul></li>
<li class="chapter" data-level="8.4" data-path="工具和框架篇.html"><a href="工具和框架篇.html#tensorflow"><i class="fa fa-check"></i><b>8.4</b> Tensorflow</a></li>
<li class="chapter" data-level="8.5" data-path="工具和框架篇.html"><a href="工具和框架篇.html#pytorch"><i class="fa fa-check"></i><b>8.5</b> Pytorch</a><ul>
<li class="chapter" data-level="8.5.1" data-path="工具和框架篇.html"><a href="工具和框架篇.html#pytorch教程"><i class="fa fa-check"></i><b>8.5.1</b> Pytorch教程</a></li>
<li class="chapter" data-level="8.5.2" data-path="工具和框架篇.html"><a href="工具和框架篇.html#pytorch安装与使用"><i class="fa fa-check"></i><b>8.5.2</b> Pytorch安装与使用</a></li>
</ul></li>
<li class="chapter" data-level="8.6" data-path="工具和框架篇.html"><a href="工具和框架篇.html#其他-4"><i class="fa fa-check"></i><b>8.6</b> 其他</a></li>
</ul></li>
<li class="chapter" data-level="9" data-path="开源项目推荐.html"><a href="开源项目推荐.html"><i class="fa fa-check"></i><b>9</b> 开源项目推荐</a></li>
<li class="chapter" data-level="10" data-path="免费资料下载.html"><a href="免费资料下载.html"><i class="fa fa-check"></i><b>10</b> 免费资料下载</a><ul>
<li class="chapter" data-level="10.1" data-path="免费资料下载.html"><a href="免费资料下载.html#python-2"><i class="fa fa-check"></i><b>10.1</b> Python</a></li>
<li class="chapter" data-level="10.2" data-path="免费资料下载.html"><a href="免费资料下载.html#机器学习-1"><i class="fa fa-check"></i><b>10.2</b> 机器学习</a></li>
<li class="chapter" data-level="10.3" data-path="免费资料下载.html"><a href="免费资料下载.html#深度学习-2"><i class="fa fa-check"></i><b>10.3</b> 深度学习</a></li>
<li class="chapter" data-level="10.4" data-path="免费资料下载.html"><a href="免费资料下载.html#其他-5"><i class="fa fa-check"></i><b>10.4</b> 其他</a></li>
<li class="chapter" data-level="10.5" data-path="免费资料下载.html"><a href="免费资料下载.html#数据集"><i class="fa fa-check"></i><b>10.5</b> 数据集</a></li>
<li class="chapter" data-level="10.6" data-path="免费资料下载.html"><a href="免费资料下载.html#r"><i class="fa fa-check"></i><b>10.6</b> R</a></li>
</ul></li>
<li class="chapter" data-level="11" data-path="机器学习论文.html"><a href="机器学习论文.html"><i class="fa fa-check"></i><b>11</b> 机器学习论文</a><ul>
<li class="chapter" data-level="11.1" data-path="机器学习论文.html"><a href="机器学习论文.html#如何高效读论文"><i class="fa fa-check"></i><b>11.1</b> 如何高效读论文？</a></li>
<li class="chapter" data-level="11.2" data-path="机器学习论文.html"><a href="机器学习论文.html#机器学习ai必读论文"><i class="fa fa-check"></i><b>11.2</b> 机器学习、AI必读论文</a></li>
<li class="chapter" data-level="11.3" data-path="机器学习论文.html"><a href="机器学习论文.html#深度学习必读论文"><i class="fa fa-check"></i><b>11.3</b> 深度学习必读论文</a></li>
</ul></li>
<li class="chapter" data-level="12" data-path="杂谈.html"><a href="杂谈.html"><i class="fa fa-check"></i><b>12</b> 杂谈</a><ul>
<li class="chapter" data-level="12.1" data-path="杂谈.html"><a href="杂谈.html#数学的故事"><i class="fa fa-check"></i><b>12.1</b> 数学的故事</a></li>
<li class="chapter" data-level="12.2" data-path="杂谈.html"><a href="杂谈.html#统计学-1"><i class="fa fa-check"></i><b>12.2</b> 统计学</a></li>
<li class="chapter" data-level="12.3" data-path="杂谈.html"><a href="杂谈.html#大厂技术观察"><i class="fa fa-check"></i><b>12.3</b> 大厂技术观察</a></li>
<li class="chapter" data-level="12.4" data-path="杂谈.html"><a href="杂谈.html#程序人生"><i class="fa fa-check"></i><b>12.4</b> 程序人生</a></li>
<li class="chapter" data-level="12.5" data-path="杂谈.html"><a href="杂谈.html#效率工具"><i class="fa fa-check"></i><b>12.5</b> 效率工具</a></li>
<li class="chapter" data-level="12.6" data-path="杂谈.html"><a href="杂谈.html#其他-6"><i class="fa fa-check"></i><b>12.6</b> 其他</a></li>
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<li class="chapter" data-level="13" data-path="联系作者.html"><a href="联系作者.html"><i class="fa fa-check"></i><b>13</b> 联系作者</a></li>
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<div id="机器学习论文" class="section level1">
<h1><span class="header-section-number">第 11 章</span> 机器学习论文</h1>
<div id="如何高效读论文" class="section level2">
<h2><span class="header-section-number">11.1</span> 如何高效读论文？</h2>
<p>来源：量子位</p>
<p><strong>第一遍：快速预览，把握概要。</strong></p>
<p>拿到一篇新论文，第一遍阅读要花多长时间？5-10分钟足以。</p>
<p>不是每一篇论文都干货满满，所以初次见面，先打个印象分，再决定是否继续，是更为高效的方法。</p>
<p>具体操作如下：</p>
<p>1、仔细阅读标题、摘要和简介。</p>
<p>2、先忽略内容，读一读文章中的每个小标题。</p>
<p>3、如果有数学内容，先大致浏览，确定其理论基础。</p>
<p>4、读结论。</p>
<p>5、浏览参考文献，如果有你已经读过的，把它们勾选出来。</p>
<p>如此读完第一遍，你需要问问自己以下几个问题：</p>
<p>1、分类：这是什么类型的论文？</p>
<p>2、背景：与哪些其他论文相关？基于何种理论基础来分析问题？</p>
<p>3、正确性：论文的假设看起来正确吗？</p>
<p>4、贡献：论文的主要贡献是什么？</p>
<p>5、清晰度：这篇论文写得好吗？</p>
<p>当你心中有了这些答案，你也就知道自己该不该真正精读这篇论文了。</p>
<p>P.S. 这里也涉及到撰写论文的一个小技巧：结构尽量清晰，要点尽量突出，让审稿人第一遍就能get到。</p>
<p><strong>第二遍：抓住重点，暂略细节</strong></p>
<p>当你判定一篇论文值得一读，就可以把它加入第二遍阅读的队列。</p>
<p>第二遍阅读，就要好好看看论文内容了，投入的时间大概在1个小时左右。</p>
<p>不过，不要纠结于没见过的术语，也不要沉迷于证明推导的细节：把它们记下来，先略过。</p>
<p>这一遍阅读中，有两个小技巧：</p>
<p>1、仔细查看论文中的图表。关注一下细节：坐标轴是否正确标记？结论是否具有统计意义？往往细节之中，就能窥见真正出色的工作和水文之间的区别。</p>
<p>2、标记论文中涉及的、你并未读过的参考文献，之后进一步阅读。</p>
<p>读完第二遍，你应该能掌握论文内容，总结全文主旨了。</p>
<p>不过，有时候即使是这样读完一遍，也未必就能读懂论文：论文可能涉及你陌生的领域，有太多陌生术语；作者可能采用了你不了解的证明或实验技术；甚至，这篇论文可能写得不行。</p>
<p>那么，就进入最后一步吧。</p>
<p><strong>第三遍：重构论文，注重细节</strong></p>
<p>要想完全理解论文，就需要展开第三遍阅读：跟随作者的思路，在脑海中重现论文内容。</p>
<p>将重现的结果与实际论文进行比较，就可以轻松看出论文的创新点，找到文中隐含的假设，捕获隐藏在实验和技术分析中的潜在问题和引文缺失。</p>
<p>进入第三遍，最重要的事情强调三遍：细节！细节！细节！</p>
<p>找出作者陈述中的每一个假设，亲自挑战它，提出自己的思考。如此，对于论文的证明和其中的技术，你便会有更为深刻的理解。</p>
<p><strong>One More Thing：文献调研怎么做？</strong></p>
<p>说到读论文，是不是想起了被文献综述统治的恐惧？</p>
<p>Srinivasan Keshav教授同样有“三步法”要传授诸位。</p>
<p><strong>首先</strong>，善用学术搜索引擎（如谷歌学术），找出3-5篇相关领域近期最高引用的论文。</p>
<p><img src="files/google.png"></p>
<p>了解这些论文的工作原理，阅读其中related work的部分。幸运的话，这些内容能直接帮你完成文献综述。</p>
<p><strong>第二步</strong>，在这些论文的参考文献中找出其共同引用的论文，或重复出现的作者姓名。</p>
<p>访问这些关键人物的网站，查看他们近期发表的论文，也可以看看他们都参加了哪些顶级会议。</p>
<p><strong>第三步</strong>，访问顶级会议的网站，浏览它们最近的会议记录。</p>
<p>通过“三遍论”的第一遍阅读快速识别高质量的相关工作。</p>
<p>汇总这一步中查找出的论文和第二步中的高引论文，基本上就能构成你文献综述的初版内容啦。</p>
<p>最后，三步法可以迭代进行。</p>
</div>
<div id="机器学习ai必读论文" class="section level2">
<h2><span class="header-section-number">11.2</span> 机器学习、AI必读论文</h2>
<p><a href="https://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648931036&amp;idx=2&amp;sn=400894afd88a44f5ec2f068ecc88b30a&amp;chksm=8794e8f6b0e361e0a518b6022669eb00c057690871814ac1d5e0cf5c926a689f7b4af54875fd&amp;token=2004915986&amp;lang=en_US#rd">人工智能必看的45篇论文</a></p>
<p><a href="https://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648942033&amp;idx=2&amp;sn=1d48e0bc87bd369a53b8128fba13a71d&amp;chksm=879437fbb0e3beed116bf0b516a730d7ddc6470fa31698ca99a7a51c7e3dda70543f0b5032b1&amp;token=281192998&amp;lang=zh_CN#rd">机器学习领域最全综述列表！</a></p>
<p><a href="https://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648940193&amp;idx=1&amp;sn=8c75061b268d3ffe3c177e8ae24ef8e0&amp;chksm=87940c8bb0e3859d6be30be1e0d82318bdc312c54392a0a9508043083e95d405d0348f074e8a&amp;token=281192998&amp;lang=zh_CN#rd">【必读经典】机器学习论文清单</a></p>
<p><a href="https://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648958102&amp;idx=2&amp;sn=4a82ea74538cc86ce4806875e7911f43&amp;chksm=879476bcb0e3ffaad1c23c04802c8b343ca95a500f9db97d4d22c99a625ec2a89ff62cd14bc1&amp;token=281192998&amp;lang=zh_CN#rd">深度学习领域，最惊艳的论文！</a></p>
<p><a href="https://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648933743&amp;idx=1&amp;sn=8d7acbe569feb4ed7099392a2f2c06fc&amp;chksm=87941745b0e39e531bd8a4c69d7f670341ad9d54c2afe2c71b32bda040fbccb07d83a769491f&amp;token=281192998&amp;lang=zh_CN#rd">全网最全的论文下载渠道（免费）</a></p>
<p><a href="https://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648958602&amp;idx=2&amp;sn=4cc4fbf12063578ea260edf8d601f848&amp;chksm=879474a0b0e3fdb6abe27ba1a2a5ad342d672bfa791b27b8888871420104514d02880f8ba2fc&amp;token=281192998&amp;lang=zh_CN#rd">李沐大佬公开课：深度学习论文精读</a></p>
<p><a href="https://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648960294&amp;idx=2&amp;sn=dc425fd5d2f35fa5436644b14ea0c021&amp;chksm=87947f0cb0e3f61aa06add0c60544ed65b4d87450a7094cd2b69b41ddf7e07b474a6dc11fd6a&amp;token=281192998&amp;lang=zh_CN#rd">2021年充满惊喜的人工智能论文综述</a></p>
<p><a href="https://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648956056&amp;idx=2&amp;sn=2f7457631f9c4b01d1be3c6dfc968469&amp;chksm=87944eb2b0e3c7a49f5d9fc7631585d5f04f5c595549fb5a44d61c84108bf91ee5b1d410907e&amp;token=281192998&amp;lang=zh_CN#rd">【PDF下载】如何写一篇牛逼的机器学习论文？17页实操指南</a></p>
<p><a href="https://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648960754&amp;idx=2&amp;sn=29913a9deb43f3813c7eba535b8a1bd1&amp;chksm=87947cd8b0e3f5cef645b9e737b647a7db5fd919acabdc547d463512aaf6cf108e23d2bcf434&amp;token=281192998&amp;lang=zh_CN#rd">覆盖近2亿篇论文还免费！沈向洋旗下团队「读论文神器」</a></p>
</div>
<div id="深度学习必读论文" class="section level2">
<h2><span class="header-section-number">11.3</span> 深度学习必读论文</h2>
<p>1 ImageNet Classification with Deep Convolutional Neural Networks</p>
<p>下载地址：<a href="http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf" class="uri">http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf</a></p>
<p>2 Using Very Deep Autoencoders for Content Based Image Retrieval</p>
<p>下载地址：<a href="http://www.cs.toronto.edu/~hinton/absps/esann-deep-final.pdf" class="uri">http://www.cs.toronto.edu/~hinton/absps/esann-deep-final.pdf</a></p>
<p>3 Learning Deep Architectures for AI</p>
<p>下载地址：<a href="http://www.iro.umontreal.ca/~lisa/pointeurs/TR1312.pdf" class="uri">http://www.iro.umontreal.ca/~lisa/pointeurs/TR1312.pdf</a></p>
<p>4 CMU’s list of papers</p>
<p>下载地址：<a href="http://deeplearning.cs.cmu.edu/" class="uri">http://deeplearning.cs.cmu.edu/</a></p>
<p>5 Neural Networks for Named Entity Recognition zip</p>
<p>下载地址：<a href="https://nlp.stanford.edu/~socherr/pa4_ner.pdf" class="uri">https://nlp.stanford.edu/~socherr/pa4_ner.pdf</a></p>
<p>6 Geoff Hinton’s reading list (all papers)</p>
<p>下载地址：<a href="http://www.cs.toronto.edu/~hinton/deeprefs.html" class="uri">http://www.cs.toronto.edu/~hinton/deeprefs.html</a></p>
<p>7 Supervised Sequence Labelling with Recurrent Neural Networks</p>
<p>下载地址：<a href="http://www.cs.toronto.edu/~graves/preprint.pdf" class="uri">http://www.cs.toronto.edu/~graves/preprint.pdf</a></p>
<p>8 Statistical Language Models based on Neural Networks</p>
<p>下载地址：<a href="http://www.fit.vutbr.cz/~imikolov/rnnlm/thesis.pdf" class="uri">http://www.fit.vutbr.cz/~imikolov/rnnlm/thesis.pdf</a></p>
<p>9 Training Recurrent Neural Networks</p>
<p>下载地址：<a href="http://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf" class="uri">http://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf</a></p>
<p>10 Recursive Deep Learning for Natural Language Processing and Computer</p>
<p>下载地址：Vision <a href="https://nlp.stanford.edu/~socherr/thesis.pdf" class="uri">https://nlp.stanford.edu/~socherr/thesis.pdf</a></p>
<p>11 Bi-directional RNN</p>
<p>下载地址：<a href="https://www.di.ufpe.br/~fnj/RNA/bibliografia/BRNN.pdf" class="uri">https://www.di.ufpe.br/~fnj/RNA/bibliografia/BRNN.pdf</a></p>
<p>12 LSTM</p>
<p>下载地址：<a href="http://web.eecs.utk.edu/~ielhanan/courses/ECE-692/Bobby_paper1.pdf" class="uri">http://web.eecs.utk.edu/~ielhanan/courses/ECE-692/Bobby_paper1.pdf</a></p>
<p>13 GRU - Gated Recurrent Unit</p>
<p>下载地址：<a href="https://arxiv.org/pdf/1406.1078v3.pdf" class="uri">https://arxiv.org/pdf/1406.1078v3.pdf</a></p>
<p>14 GFRNN</p>
<p>下载地址：<a href="https://arxiv.org/pdf/1502.02367v3.pdf" class="uri">https://arxiv.org/pdf/1502.02367v3.pdf</a></p>
<p>15 LSTM: A Search Space Odyssey</p>
<p>下载地址：<a href="https://arxiv.org/pdf/1503.04069v1.pdf" class="uri">https://arxiv.org/pdf/1503.04069v1.pdf</a></p>
<p>16 A Critical Review of Recurrent Neural Networks for Sequence Learning</p>
<p>下载地址：<a href="https://arxiv.org/pdf/1506.00019v1.pdf" class="uri">https://arxiv.org/pdf/1506.00019v1.pdf</a></p>
<p>17 Visualizing and Understanding Recurrent Networks</p>
<p>下载地址：<a href="https://arxiv.org/pdf/1506.02078v1.pdf" class="uri">https://arxiv.org/pdf/1506.02078v1.pdf</a></p>
<p>18 Wojciech Zaremba, Ilya Sutskever, An Empirical Exploration of Recurrent Network Architectures</p>
<p>下载地址：<a href="http://proceedings.mlr.press/v37/jozefowicz15.pdf" class="uri">http://proceedings.mlr.press/v37/jozefowicz15.pdf</a></p>
<p>19 Recurrent Neural Network based Language Model</p>
<p>下载地址: <a href="http://www.fit.vutbr.cz/research/groups/speech/publi/2010/mikolov_interspeech2010_IS100722.pdf" class="uri">http://www.fit.vutbr.cz/research/groups/speech/publi/2010/mikolov_interspeech2010_IS100722.pdf</a></p>
<p>20 Extensions of Recurrent Neural Network Language Model</p>
<p>下载地址：<a href="http://www.fit.vutbr.cz/research/groups/speech/publi/2011/mikolov_icassp2011_5528.pdf" class="uri">http://www.fit.vutbr.cz/research/groups/speech/publi/2011/mikolov_icassp2011_5528.pdf</a></p>
<p>21 Recurrent Neural Network based Language Modeling in Meeting Recognition</p>
<p>下载地址：<a href="http://www.fit.vutbr.cz/~imikolov/rnnlm/ApplicationOfRNNinMeetingRecognition_IS2011.pdf" class="uri">http://www.fit.vutbr.cz/~imikolov/rnnlm/ApplicationOfRNNinMeetingRecognition_IS2011.pdf</a></p>
<p>22 Deep Neural Networks for Acoustic Modeling in Speech Recognition Speech</p>
<p>下载地址：<a href="http://cs224d.stanford.edu/papers/maas_paper.pdf" class="uri">http://cs224d.stanford.edu/papers/maas_paper.pdf</a></p>
<p>23 Reinforcement Learning Neural Turing Machines</p>
<p>下载地址：<a href="https://arxiv.org/pdf/1505.00521v1.pdf" class="uri">https://arxiv.org/pdf/1505.00521v1.pdf</a></p>
<p>24 Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation</p>
<p>下载地址：<a href="https://arxiv.org/pdf/1406.1078v3.pdf" class="uri">https://arxiv.org/pdf/1406.1078v3.pdf</a></p>
<p>25 Google - Sequence to Sequence Learning with Neural Networks</p>
<p>下载地址：<a href="http://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf" class="uri">http://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf</a></p>
<p>26 Memory Networks</p>
<p>下载地址：<a href="https://arxiv.org/pdf/1410.3916v10.pdf" class="uri">https://arxiv.org/pdf/1410.3916v10.pdf</a></p>
<p>27 Policy Learning with Continuous Memory States for Partially Observed Robotic Control</p>
<p>下载地址：<a href="https://arxiv.org/pdf/1507.01273v1.pdf" class="uri">https://arxiv.org/pdf/1507.01273v1.pdf</a></p>
<p>28 Microsoft - Jointly Modeling Embedding and Translation to Bridge Video and Language</p>
<p>下载地址：<a href="https://arxiv.org/pdf/1505.01861v1.pdf" class="uri">https://arxiv.org/pdf/1505.01861v1.pdf</a></p>
<p>29 Neural Turing Machines</p>
<p>下载地址：<a href="https://arxiv.org/pdf/1410.5401v2.pdf" class="uri">https://arxiv.org/pdf/1410.5401v2.pdf</a></p>
<p>30 Ask Me Anything: Dynamic Memory Networks for Natural Language Processing</p>
<p>下载地址：<a href="https://arxiv.org/pdf/1506.07285v1.pdf" class="uri">https://arxiv.org/pdf/1506.07285v1.pdf</a></p>
<p>31 Mastering the Game of Go with Deep Neural Networks and Tree Search</p>
<p>下载地址：<a href="https://www.nature.com/articles/nature16961" class="uri">https://www.nature.com/articles/nature16961</a></p>
<p>32 Batch Normalization</p>
<p>下载地址：<a href="https://arxiv.org/abs/1502.03167" class="uri">https://arxiv.org/abs/1502.03167</a></p>
<p>33 Residual Learning</p>
<p>下载地址：<a href="https://arxiv.org/pdf/1512.03385v1.pdf" class="uri">https://arxiv.org/pdf/1512.03385v1.pdf</a></p>
<p>34 Image-to-Image Translation with Conditional Adversarial Networks</p>
<p>下载地址：<a href="https://arxiv.org/pdf/1611.07004v1.pdf" class="uri">https://arxiv.org/pdf/1611.07004v1.pdf</a></p>
<p>35 Berkeley AI Research (BAIR) Laboratory</p>
<p>下载地址：<a href="https://arxiv.org/pdf/1611.07004v1.pdf" class="uri">https://arxiv.org/pdf/1611.07004v1.pdf</a></p>
<p>36 MobileNets by Google</p>
<p>下载地址：<a href="https://arxiv.org/abs/1704.04861" class="uri">https://arxiv.org/abs/1704.04861</a></p>
<p>37 Cross Audio-Visual Recognition in the Wild Using Deep Learning</p>
<p>下载地址：<a href="https://arxiv.org/abs/1706.05739" class="uri">https://arxiv.org/abs/1706.05739</a></p>
<p>38 Dynamic Routing Between Capsules</p>
<p>下载地址：<a href="https://arxiv.org/abs/1710.09829" class="uri">https://arxiv.org/abs/1710.09829</a></p>
<p>39 Matrix Capsules With Em Routing</p>
<p>下载地址：<a href="https://openreview.net/pdf?id=HJWLfGWRb" class="uri">https://openreview.net/pdf?id=HJWLfGWRb</a></p>
<p>40 Efficient BackProp</p>
<p>下载地址：<a href="http://yann.lecun.com/exdb/publis/pdf/lecun-98b.pdf" class="uri">http://yann.lecun.com/exdb/publis/pdf/lecun-98b.pdf</a></p>
<p>41 Recognition with Deep Recurrent Neural Networks</p>
<p>下载地址：<a href="http://cs224d.stanford.edu/papers/maas_paper.pdf" class="uri">http://cs224d.stanford.edu/papers/maas_paper.pdf</a></p>

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